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Deep Learning-Based Crack Detection for Cultural Heritage Structures: Case Study of Ghana's Independence Arch Using Convolutional Neural Networks

Domain:

digital infrastructure

Record type:

datasetmodelsoftwarepaper
Creator:
Asa
Publisher:
Zenodo
Host:avatar

This repository presents a comprehensive computer vision methodology for automated crack detection in concrete cultural heritage structures, applied to Ghana's Independence Arch (Accra). The study addresses critical conservation challenges for marine-exposed monuments through UAV-based image acquisition and deep learning classification. Key contributions include: site-specific environmental analysis (extreme marine exposure per GB 5009-2012, ASCE-7 wind loading); Convolutional Neural Network architecture optimized for concrete crack detection (94% validation accuracy); complete image processing pipeline: augmentation, normalization, whitening, and binary classification; practical damage control framework integrating detection with repair strategies (epoxy injection, self-healing concrete); economic impact assessment linking structural conservation to tourism revenue. The methodology demonstrates scalable, low-cost structural health monitoring for developing nation heritage infrastructure.

Visit

doi.org

Tasks

computer visionimage classification

Languages

Ga

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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